1. Field of the Invention
The present invention relates to a temperature controlling apparatus using a fuzzy inference and a method using the same, and more particularly, to a temperature controlling apparatus adopting a fuzzy adaptation model in which temperatures of a plurality of positions of a refrigeration compartment are estimated in order to rapidly reach temperature equilibrium in the refrigeration compartment, reflecting the operation states of a compressor and a cooling fan which directly affect the temperature of the refrigeration compartment, and a method using the same.
2. Description of the Related Art
In general, as shown in FIG. 1, a refrigerator includes a main body 4, a freezer compartment door 6 and a refrigeration compartment door 7. Here, the main body 4 with an insulation structure has a freezer compartment 2 and a refrigeration compartment 3 which are separated by a partition 1. The main body 4 includes a cabinet 4a for forming the overall frame, a liner 4b arranged inside the cabinet 4a, and a foam element 4c filling the space between the cabinet 4a and the liner 4b.
A compressor 11 is installed in a machinery compartment formed at the lower portion of the refrigeration compartment 3, and a condenser and an expansion valve are installed in the main body of the machinery compartment, with an evaporator 12 installed at the rear wall of the freezer compartment 2, all of which are connected to each other by a refrigerant tube, thereby achieving a freezing circulation cycle. A cooling fan 13 is installed over the evaporator 12, such that cool air generated by the evaporator 12 is forcibly ventilated into the freezer compartment 2 and the refrigeration compartment 3. In order to guide the supply of cool air, a fan guide 14 is installed in front of the cooling fan 13, and a duct 15a is provided at the rear wall of the refrigeration compartment 3. A cool air control damper 19 controls the amount of cool air provided to the refrigeration compartment 3, and a plurality of shelfs 8 is for receiving foodstuffs.
In the refrigerator having the above simple structure, in order to improve cooling efficiency, there is provided a refrigerator capable of controlling a cool air discharge direction shown in FIG. 3, where a cool air discharge control blade 18 as shown in FIG. 2 is installed in the duct 15a. In such a refrigerator, a housing 17 having a cool air discharge path (not shown) and a discharge hole 16 is installed at the rear wall of the refrigeration compartment 3 in order to guide the supply of cool air. As shown in FIG. 4, such housing 17 is installed at the center of the rear wall of the refrigeration compartment 3, such that the cool air discharge direction into the refrigeration compartment is controlled according to the rotary position of the cool air discharge control blade 18. As a result, the cool air can be intensively discharged into a high-temperature position. A conventional method adopts a generic algorithm (GA)-fuzzy inference as shown in FIG. 5, in order to control the cool air discharge direction.
According to this method, first, Te (T1 and T2) is inferred using a first GA-fuzzy function, and the optimal cool air discharge direction is selected by applying a second GA-fuzzy function. Here, T1 and T2 are inferred temperatures at the right wall corresponding to 1H/3 of the refrigeration compartment 3 and the left wall corresponding to 3H/4 of the refrigeration compartment 3, where H represents the height of the refrigeration compartment 3. T1 and T2 are inferred from inputs R1 and R2 using the GA-fuzzy function, wherein R1 is the temperature sensed by a temperature sensor 53 set at the right wall, corresponding to the 1H/3, of the refrigeration compartment 3, and R2 is a temperature value sensed by a temperature sensor set at the left wall 52, corresponding to the 3H/4, of the refrigeration compartment 3. Tr represents a reference temperature pattern data according to the cool air discharge direction, which are learned depending on changes in R1 and R2. These data are obtained through various experiments considering changes in external temperature, temperature distribution of foodstuffs stored in the refrigeration compartment 3 and temperature change rate in the fuzzy concept, which correspond to a rule of thumb obtained from an expert's experiences.
Also, in a fuzzy model identifier 51, a fuzzy membership function is installed for determining the temperature of load (foodstuffs to be refrigerated) put in the refrigeration compartment 3, that is, whether the foodstuffs are hot, warm, moderate or cold.
In the above conventional temperature controlling method, T1 and T2, which are temperatures at 1H/3 of the right wall of the refrigeration compartment 3 and 3H/4 of the left wall of the refrigeration compartment 3, respectively, are inferred from R1 and R2 measured by two temperature sensors 53, 52 installed at 1H/3 of the right wall and 3H/4 of the left wall of the refrigeration compartment 3, respectively, using the GA-Fuzzy mode. Also, a stationary angle of the rotary blade 18a is inferred by using the temperatures measured by the sensors 52, 53, the inferred temperatures and the difference between the measured and inferred temperatures, as input values of the fuzzy model. The TSK model has been used as the fuzzy model, which is excellent for expressing non-linear systems. However, it is difficult to obtain the optimal values of the parameters in a precondition part from the TSK model, so that the parameters of the precondition part are obtained using the GA algorithm.
However, the model used for the inference of temperature in the above control method uses a static model to estimate the internal temperature of the refrigeration compartment 3. Also, there is no concern about the operational conditions of the compressor 11 and cooling fan 13 which directly affect the change in internal temperature of the refrigeration compartment 3. That is, the temperatures of predetermined portions are estimated only using the values measured by the sensor 52 or 53. However, in this case, since a factor for changing temperature is not included, there is a serious error in the estimation of temperature. Also, since the parameters are defined by an off-line method, characteristics of each set of refrigerators cannot be considered.
Also, since the estimated temperature is used as an input of a fuzzy controller, accuracy in estimation of temperature is required. However, due to the above-described problems, it is difficult to achieve accurate control.